Parakeet Podcast Processor
Description
šļø PĀ³: Lightning-fast podcast processing with Apple Silicon optimization and local LLMs. Parakeet MLX transcription + Ollama analysis = structured podcast summaries in minutes. 100% local, no API keys required.
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
Parakeet Podcast Processor (PĀ³)
**Automated podcast processing with Apple Silicon optimization and local LLMs**
Transform podcasts into structured summaries using cutting-edge Apple Silicon ML acceleration.
**Inspired by [Tomasz Tunguz](https://tomtunguz.com)**'s innovative podcast processing system described in his "How I AI" interview. This implementation builds on his pioneering work in automated podcast analysis for venture capital and business intelligence.
ā” Key Features
- š§ Smart Audio Processing: RSS feed monitoring + ffmpeg normalization
- š Lightning Fast Transcription: Parakeet MLX (30x faster than Whisper on Apple Silicon)
- š§ Local LLM Analysis: Ollama integration for structured summarization
- āļø AI Blog Generation: Iterative writing with AP English teacher grading system
- š± Social Media Posts: Auto-generate Twitter and LinkedIn content
- š¾ Efficient Storage: DuckDB for fast queries and analysis
- š Rich Outputs: Markdown and JSON exports with topics, themes, quotes, and company mentions
- š 100% Local: No API keys required, complete privacy
š¦ Quick Start
# Prerequisites: macOS with Apple Silicon + ffmpeg + Ollama
brew install ffmpeg
# Install Ollama from https://ollama.com, then: ollama pull llama3.2
# Setup PĀ³
python3 -m venv venv && source venv/bin/activate
pip install -e .
p3 init
# Configure feeds in config/feeds.yaml
# Then run the complete pipeline:
p3 fetch && p3 transcribe && p3 digest && p3 export
# Generate blog posts from digest (Tunguz's innovation):
p3 write --topic "AI's Impact on Software Development"
# Or run the demo script:
python demo.pyā” Performance
- Audio Download: ~30 seconds per episode
- Parakeet Transcription: 60 minutes audio ā 1 second processing
- Ollama Analysis: Full transcript ā structured summary in ~10 seconds
- Total Pipeline: ~1 minute for complete podcast processing
šļø Architecture
RSS ā ffmpeg ā Parakeet MLX Related Skills
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